Tooth Discoloration Potential of Coffee With Common Additives and the Effectiveness of Whitening Toothpastes in Stain Removal

ABSTRACT Objective This study aimed to evaluate the influence of common coffee additives on tooth discolouration and assess the effectiveness of two commercially available whitening toothpastes in removing these stains. Materials and Methods Sixty bovine enamel specimens were randomly assigned to six groups ( n = 10 per group) according to the coffee solution used: black coffee (control), coffee with sugar, stevia, Sweet'N Low, Splenda, or cream. Specimens were immersed for 7 days, and colour measurements were obtained every 24 h using a spectrophotometer and quantified using the CIEDE2000 colour difference formula (ΔE 00 ). After staining, specimens were brushed with either a hydrogen peroxide–based toothpaste (V) or an abrasive‐based toothpaste (WS) for up to 10,000 cycles, with colour measurements repeated during the brushing phase (1000, 3000, 5000). An additional 18 specimens ( n = 3 per group) were sectioned to evaluate stain penetration depth using a microspectrophotometer. Colour change data were analysed using a linear mixed‐effects model and one‐way ANOVA with Tukey's post hoc test ( α = 0.05). Results The linear mixed‐effects model showed that tooth discoloration increased significantly with immersion time ( β = 1.27, p < 0.001). A significant interaction between additive group and immersion time was observed for the cream group ( β = −0.776, p < 0.001), indicating a slower rate of discoloration than the control. After 7 days, one‐way ANOVA revealed significant differences among additive groups ( p = 0.017), with the cream group showing the least discoloration (ΔE 00 = 7.26 ± 2.34) and the Splenda group the greatest (ΔE 00 = 14.48 ± 0.91). Brushing significantly reduced discoloration across cycles ( β = 0.338, p < 0.001), but no significant differences were observed between toothpaste formulations ( p = 0.443) or in the toothpaste–cycle interaction ( p = 0.306). Cross‐sectional analysis showed pigment penetration into enamel and dentine, with the cream group exhibiting the lowest subsurface discoloration. Conclusion Coffee additives affect the degree of tooth discoloration, with cream showing a protective effect. Both whitening toothpastes were effective, but the hydrogen peroxide formulation generally performed slightly better, potentially due to its deeper stain removal capability.

Authors

Institutions

Publication Details

Journal
International Journal of Dental Hygiene
Published
2026-09-08
DOI
https://doi.org/10.1111/idh.70160
Primary Topic
Dental Erosion and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Tooth Discoloration Potential of Coffee With Common Additives and the Effectiveness of Whitening Toothpastes in Stain Removal

Wanki Lee, Young‐Seok Park, Van Mai Truong, Napas Lappanakokiat et al.
International Journal of Dental Hygiene
Dental Erosion and Treatment
article

Tooth Discoloration Potential of Coffee With Common Additives and the Effectiveness of Whitening Toothpastes in Stain Removal

Wanki Lee, Young‐Seok Park, Van Mai Truong, Napas Lappanakokiat, Chang-Ha Lee, Soyeon Kim
article en

Abstract

ABSTRACT Objective This study aimed to evaluate the influence of common coffee additives on tooth discolouration and assess the effectiveness of two commercially available whitening toothpastes in removing these stains. Materials and Methods Sixty bovine enamel specimens were randomly assigned to six groups ( n = 10 per group) according to the coffee solution used: black coffee (control), coffee with sugar, stevia, Sweet'N Low, Splenda, or cream. Specimens were immersed for 7 days, and colour measurements were obtained every 24 h using a spectrophotometer and quantified using the CIEDE2000 colour difference formula (ΔE 00 ). After staining, specimens were brushed with either a hydrogen peroxide–based toothpaste (V) or an abrasive‐based toothpaste (WS) for up to 10,000 cycles, with colour measurements repeated during the brushing phase (1000, 3000, 5000). An additional 18 specimens ( n = 3 per group) were sectioned to evaluate stain penetration depth using a microspectrophotometer. Colour change data were analysed using a linear mixed‐effects model and one‐way ANOVA with Tukey's post hoc test ( α = 0.05). Results The linear mixed‐effects model showed that tooth discoloration increased significantly with immersion time ( β = 1.27, p < 0.001). A significant interaction between additive group and immersion time was observed for the cream group ( β = −0.776, p < 0.001), indicating a slower rate of discoloration than the control. After 7 days, one‐way ANOVA revealed significant differences among additive groups ( p = 0.017), with the cream group showing the least discoloration (ΔE 00 = 7.26 ± 2.34) and the Splenda group the greatest (ΔE 00 = 14.48 ± 0.91). Brushing significantly reduced discoloration across cycles ( β = 0.338, p < 0.001), but no significant differences were observed between toothpaste formulations ( p = 0.443) or in the toothpaste–cycle interaction ( p = 0.306). Cross‐sectional analysis showed pigment penetration into enamel and dentine, with the cream group exhibiting the lowest subsurface discoloration. Conclusion Coffee additives affect the degree of tooth discoloration, with cream showing a protective effect. Both whitening toothpastes were effective, but the hydrogen peroxide formulation generally performed slightly better, potentially due to its deeper stain removal capability.

International Journal of Dental Hygiene
Vietnam National University Ho Chi Minh City (VN), Seoul National University (KR), Seoul National University Dental Hospital (KR)
Openalex Percentile: Top 8%
Dental Erosion and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.